Environmental and geospatial factors drive juvenile Chinook salmon distribution during early ocean migration
Bibliographic record
Abstract
Migrating animals rely on a variety of cues to guide them, but the relative importance of those signals may vary with size, life stage, or location. During their initial ocean migration, yearling Chinook salmon (Oncorhynchus tshawytcha) from the Columbia River have stock-specific spatial distributions that shift through time. We used a two-process mixture model to examine how the distribution of yearling migrants from three Chinook salmon stocks varies as a function of geospatial (e.g., latitude and distance from shore) and environmental (e.g., chlorophyll a and temperature) covariates. In this framework, one process described the probability of being inside the spatial, temporal, and environmental boundaries of the migration route, and one process described the patchy distribution of salmon abundance within that route. We found that both environmental and geospatial covariates explained substantial portions of observed spatial patterns in abundance, suggesting that these stocks responded to multiple cues during migration. However, model selection criteria indicated that fish distributions were more affected by geospatial than by environmental covariates. We conclude that during migration, behavioral responses to environmental variation are secondary to responses to geospatial variation, sometimes resulting in suboptimal environmental conditions. This may have sublethal effects on growth and could ultimately influence stock-specific responses to broad-scale climate changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".